Genetic Algorithm Segmentation in Partial Least Squares Structural Equation Modeling

Abstract

When applying the partial least squares structural equation modeling (PLSSEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an assessment of this novel approach. PLS-GAS allows for uncovering unobserved heterogeneity and identifying different groups within a data set. In an application on customer satisfaction data and the American customer satisfaction index model, the method identifies distinctive group-specific PLS path model estimates which allow for a further differentiated interpretation of the results.

Keywords: genetic algorithm, partial least squares, path modeling, PLS-SEM, segmentation, structural equation modeling

JEL Classification: A00